90 citations · 201 across the 13 of their papers we have counts for
13 papers
AnoFel: Supporting Anonymity for Privacy-Preserving Federated Learning
Ghada Almashaqbeh, Zahra Ghodsi
Federated learning enables users to collaboratively train a machine learning model over their private datasets. Secure aggregation protocols are employed to mitigate information le…
Characterizing and Optimizing End-to-End Systems for Private Inference
Karthik Garimella, Zahra Ghodsi, Nandan Kumar Jha +2
In two-party machine learning prediction services, the client's goal is to query a remote server's trained machine learning model to perform neural network inference in some applic…
zPROBE: Zero Peek Robustness Checks for Federated Learning
Zahra Ghodsi, Mojan Javaheripi, Nojan Sheybani +3
Privacy-preserving federated learning allows multiple users to jointly train a model with coordination of a central server. The server only learns the final aggregation result, thu…
CryptoNite: Revealing the Pitfalls of End-to-End Private Inference at Scale
Karthik Garimella, Nandan Kumar Jha, Zahra Ghodsi +2
The privacy concerns of providing deep learning inference as a service have underscored the need for private inference (PI) protocols that protect users' data and the service provi…
Sphynx: ReLU-Efficient Network Design for Private Inference
Minsu Cho, Zahra Ghodsi, Brandon Reagen +2
The emergence of deep learning has been accompanied by privacy concerns surrounding users' data and service providers' models. We focus on private inference (PI), where the goal is…
Circa: Stochastic ReLUs for Private Deep Learning
Zahra Ghodsi, Nandan Kumar Jha, Brandon Reagen +1
The simultaneous rise of machine learning as a service and concerns over user privacy have increasingly motivated the need for private inference (PI). While recent work demonstrate…
Generating and Characterizing Scenarios for Safety Testing of Autonomous Vehicles
Zahra Ghodsi, Siva Kumar Sastry Hari, Iuri Frosio +5
Extracting interesting scenarios from real-world data as well as generating failure cases is important for the development and testing of autonomous systems. We propose efficient m…
DeepReDuce: ReLU Reduction for Fast Private Inference
Nandan Kumar Jha, Zahra Ghodsi, Siddharth Garg +1
The recent rise of privacy concerns has led researchers to devise methods for private neural inference -- where inferences are made directly on encrypted data, never seeing inputs.…
CryptoNAS: Private Inference on a ReLU Budget
Zahra Ghodsi, Akshaj Veldanda, Brandon Reagen +1
Machine learning as a service has given raise to privacy concerns surrounding clients' data and providers' models and has catalyzed research in private inference (PI): methods to p…
Outsourcing Private Machine Learning via Lightweight Secure Arithmetic Computation
Siddharth Garg, Zahra Ghodsi, Carmit Hazay +3
In several settings of practical interest, two parties seek to collaboratively perform inference on their private data using a public machine learning model. For instance, several…
ThUnderVolt: Enabling Aggressive Voltage Underscaling and Timing Error Resilience for Energy Efficient Deep Neural Network Accelerators
Jeff Zhang, Kartheek Rangineni, Zahra Ghodsi +1
Hardware accelerators are being increasingly deployed to boost the performance and energy efficiency of deep neural network (DNN) inference. In this paper we propose Thundervolt, a…
Optimal Checkpointing for Secure Intermittently-Powered IoT Devices
Zahra Ghodsi, Siddharth Garg, Ramesh Karri
Energy harvesting is a promising solution to power Internet of Things (IoT) devices. Due to the intermittent nature of these energy sources, one cannot guarantee forward progress o…
SafetyNets: Verifiable Execution of Deep Neural Networks on an Untrusted Cloud
Zahra Ghodsi, Tianyu Gu, Siddharth Garg
Inference using deep neural networks is often outsourced to the cloud since it is a computationally demanding task. However, this raises a fundamental issue of trust. How can a cli…